{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#DataEDA\n",
    "# week Two homeWork\n",
    "# Part One EDA Data\n",
    "# Data description: continues and category features\n",
    "# part-one:EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data manipulation \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (40, 20),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data from file\n",
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()\n",
    "# print data shape of training Data\n",
    "# print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#countplot\n",
    "sn.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "# input data info file\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 文档说明 0.00000为空值，无意义的值可以当作NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# featureEDA:\n",
    "# 1) Categorical variables\n",
    "# 2) Numeric variables\n",
    "# 3) Numeric feature with numerical response\n",
    "# 4) Categorical feature with numerical response\n",
    "# 5) Numeric feature with categorical response\n",
    "# 6) Categorical feature with categorical response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pregnants features counts\n",
      "1     135\n",
      "0     111\n",
      "2     103\n",
      "3      75\n",
      "4      68\n",
      "5      57\n",
      "6      50\n",
      "7      45\n",
      "8      38\n",
      "9      28\n",
      "10     24\n",
      "11     11\n",
      "13     10\n",
      "12      9\n",
      "14      2\n",
      "15      1\n",
      "17      1\n",
      "Name: pregnants, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 可能分类特征 pregnants\n",
    "categorical_features = ['pregnants']\n",
    "for col in categorical_features:\n",
    "    print '\\n%s features counts'%col\n",
    "    print train[col].value_counts()\n",
    "    train[col] = train[col].astype('object') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'pregnants distribution of Target')]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rt373L9+vVJ61+8eEEq\nlSIcDrN27dpZ1ZYkSZIkSZorPiUmSZIkSZI0Dy1btoxTp05x9epVKioqePz4Ma9fvyYajdLe3j6t\nPdasWUNbWxvHjx9n3759rF69mmg0Sjqd5smTJwAcO3Zs0s09R48eJZVKce3aNR48eEBVVRXv378n\nmUwSjUZ59eoVixcvnvG5GhsbOXv2LMPDw9TV1bFu3Tqy2SyDg4OMjo6ycuVKnj9/zsePH2dcA6Ci\nogKAIAh48+YNmzZtYvv27bS3t9PS0sKhQ4c4d+4c0WiUsbExBgYGGB8f58CBA5SXl8+qtiRJkiRJ\n0lzxxiBJkiRJkqR5aMuWLZw8eRIg/3zYnj17CIKAsrKyae+zY8cOLl++TH19PSMjI9y8eZPR0VFq\na2u5dOkS8Xh80vqlS5cSBAHNzc2EQiH6+voYGRmhtbWV1tZWAEpKSmZ8rpKSErq7u2lqaqK4uJjb\nt2+TTCZZsWIFHR0dXLlyheLiYlKp1KTbfn5VQ0MD8Xg8X2NgYACA+vp6EokENTU1vHv3jp6eHoaG\nhqiurubMmTO0tLTMuKYkSZIkSdJcC+VyuVyhm5AkSZIkSdL0nD59mq6uLvbu3ZsP4syVTCbD8PAw\nZWVlRCKRKfMXLlygs7OT/fv3c/jw4TntTZIkSZIkSVN5Y5AkSZIkSZKmJZvN0tTURG1tLel0etLc\ny5cvSSQShEIh6urqCtShJEmSJEmS/uuvQjcgSZIkSZKk+aGoqIh4PE4ikaChoYH169dTWlrKhw8f\nePToERMTExw8eJDKyspCtypJkiRJkiQMBkmSJEmSJOkXHDlyhKqqKoIg4NmzZ3z69IlIJEIsFqO5\nuZlYLFboFiVJkiRJkvSvUC6XyxW6CUmSJEmSJEmSJEmSJEm/V7jQDUiSJEmSJEmSJEmSJEn6/QwG\nSZIkSZIkSZIkSZIkSQuQwSBJkiRJkiRJkiRJkiRpATIYJEmSJEmSJEmSJEmSJC1ABoMkSZIkSZIk\nSZIkSZKkBehvS/4ZAVI959MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['pregnants','Target']],x='pregnants',y='Target',hue='pregnants',ax=ax)\n",
    "ax.set(title=\"pregnants distribution of Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 怀孕次数和患糖尿病有一定的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "numericTable = train.drop(['pregnants'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 8 columns):\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 48.1 KB\n"
     ]
    }
   ],
   "source": [
    "numericTable.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Plasma_glucose_concentration  blood_pressure  \\\n",
       "count                    768.000000      768.000000   \n",
       "mean                     120.894531       69.105469   \n",
       "std                       31.972618       19.355807   \n",
       "min                        0.000000        0.000000   \n",
       "25%                       99.000000       62.000000   \n",
       "50%                      117.000000       72.000000   \n",
       "75%                      140.250000       80.000000   \n",
       "max                      199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numericTable.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 缺失值处理\n",
    "# verOne\n",
    "# 用中位数代替\n",
    "# verTwo\n",
    "# 增加一列标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# NaN预处理\n",
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "numericTable[NaN_col_names] = numericTable[NaN_col_names].replace(0, np.NaN)\n",
    "print(numericTable.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Triceps_skin_fold_thickness  Triceps_skin_fold_thickness_Missing\n",
       "0                         35.0                                    0\n",
       "1                         29.0                                    0\n",
       "2                          NaN                                    1\n",
       "3                         23.0                                    0\n",
       "4                         35.0                                    0\n",
       "5                          NaN                                    1\n",
       "6                         32.0                                    0\n",
       "7                          NaN                                    1\n",
       "8                         45.0                                    0\n",
       "9                          NaN                                    1"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看缺失值对目标的影响\n",
    "# 表明是缺失值还是不是缺失值\n",
    "numericTable['Triceps_skin_fold_thickness_Missing'] = numericTable['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "numericTable[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cbdd090>"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TC1wvEREpc/kESBvq7qpaTt1XZ4mISBOWT4D8FRhjZh1SA8xsB+A64JkC10tERMpcPudA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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "%matplotlib inline\n",
    "sns.countplot(x=\"Triceps_skin_fold_thickness_Missing\", hue=\"Target\",data=numericTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cbe5a50>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AKcDTQCUQ+zRWE/YFlhJOVW0ELgOeMrP9gC7RPDUpy9QCpelWUFOTurjEpc9Sipn2z/zI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\nABERkVj+D4xeBiVQ6mdpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值比较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "numericTable['serum_insulin_Missing'] = numericTable['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"serum_insulin_Missing\", hue=\"Target\",data=numericTable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 表明缺失值与目标之间没有关系\n",
    "numericTable.drop([\"Triceps_skin_fold_thickness_Missing\", \"serum_insulin_Missing\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 用中位数填值\n",
    "medians = numericTable.median() \n",
    "numericTable = numericTable.fillna(medians)\n",
    "print(numericTable.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Triceps_skin_fold_thickness and BMI = 0.54\n"
     ]
    }
   ],
   "source": [
    "# correlation for numeric variable\n",
    "cols = numericTable.columns\n",
    "data_corr = numericTable.corr()\n",
    "data_corr = data_corr.abs()\n",
    "# plt.subplots(figize = (13,9))\n",
    "# sns.heatmap(data_corr, annot = True)\n",
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plasma_glucose_concentration    0\n",
    "# blood_pressure                  0\n",
    "# Triceps_skin_fold_thickness     0\n",
    "# serum_insulin                   0\n",
    "# BMI                             0\n",
    "# Diabetes_pedigree_function      0\n",
    "# Age                             0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mw4EjgNXj5DmE7k1mVis4ERHJr+RmvO5+EnCSma0BtLq7ujARERnAejMeCADu/lo1AhER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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numeric variable \n",
    "fig = plt.figure()\n",
    "sns.distplot(numericTable.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of blood_pressure')"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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abjMbQmjjPe5+ZV75mcBRwAjgDuA4d18WW18Nemq7mX0UuIAwQrUReBg43t3/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\nASMztjaYd38rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(numericTable.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('Number of blood_pressure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S+hVwLlAVqPF0IvDrqEUVR4YPH86YMWMASN6yAnvlLosjigFfPe4NH2Ez/HTu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\nWP4/tN7Dk9ORw1kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='blood_pressure', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P065du5jHEm7CWKKUeh/4FAiNvmitP65sw8GuqNuB0i1fSzUFirXWZXcZ2Re8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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yf+jYoUOHUm7rRyGSyeLFiyMDTCafWNHkkVH16ZRhML7A7AD/4IMP2Lt3b+u8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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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24NmCUeMWKivzwis5Ccxck0JReWBcwX333cf06dNp2bKlsTuvo0YbCBUTRFld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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Iaa13ApuAM2ocPhVzeu3iWF0nGa1YsQKA6syOYKtdcymYlQeYXVDBYMMbpgsh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\nCCGEqJckCCGEEPWSBCGEEKJe/w92tyzPebUSMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Diabetes_pedigree_function', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Diabetes_pedigree_function', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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67fceQoh9tm7dGl1w6sQe9iyNGW+Rz/HZZ59hGIkfZR2/pvIDUEr9CbhCaz0o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\nACQQhBBChEkgCCGEACQQhBBChP3/+53JNUrEsUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Age', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c42fbd0>"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAWrilFmwgv42h3KDCd5XOSAvPiRNaZziT/1uvfveE8AZcKiJs+5swo3yQ8no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\n/Udy4EAi48e9wwujBtD9qU6kpKTQ/Yl+uV4jI3sKKE7+N6C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KwJeFelaGYRQqBRX/LiJx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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numeric variable and response\n",
    "# temp\tatemp\thum--windspeed\tcasual\tregistered\tcnt\n",
    "corrMatt = numericTable[[\"Plasma_glucose_concentration\",\"blood_pressure\",\n",
    "                    \"Triceps_skin_fold_thickness\",\"serum_insulin\",\n",
    "                    \"BMI\",\"Diabetes_pedigree_function\",\"Age\",\n",
    "                    \"Target\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# training model data pre\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>pregnants_2</th>\n",
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       "      <th>pregnants_4</th>\n",
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       "      <th>pregnants_17</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_0  pregnants_1  pregnants_2  pregnants_3  pregnants_4  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            1            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            1            0            0            0   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   pregnants_5  pregnants_6  pregnants_7  pregnants_8  pregnants_9  \\\n",
       "0            0            1            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            1            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   pregnants_10  pregnants_11  pregnants_12  pregnants_13  pregnants_14  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   pregnants_15  pregnants_17  \n",
       "0             0             0  \n",
       "1             0             0  \n",
       "2             0             0  \n",
       "3             0             0  \n",
       "4             0             0  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model file pre for category features\n",
    "categorical_features = ['pregnants']\n",
    "\n",
    "#数据类型变为object，才能被get_dummies处理\n",
    "for col in categorical_features:\n",
    "    train[col] = train[col].astype('object')\n",
    "    \n",
    "X_train_cat = train[categorical_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n",
    "X_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = numericTable['Target']\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numericTable = numericTable.drop(['Target'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose_concentration  blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "0                      0.866045       -0.031990                     0.670643   \n",
       "1                     -1.205066       -0.528319                    -0.012301   \n",
       "2                      2.016662       -0.693761                    -0.012301   \n",
       "3                     -1.073567       -0.528319                    -0.695245   \n",
       "4                      0.504422       -2.679076                     0.670643   \n",
       "\n",
       "   serum_insulin       BMI  Diabetes_pedigree_function       Age  \n",
       "0      -0.181541  0.166619                    0.468492  1.425995  \n",
       "1      -0.181541 -0.852200                   -0.365061 -0.190672  \n",
       "2      -0.181541 -1.332500                    0.604397 -0.105584  \n",
       "3      -0.540642 -0.633881                   -0.920763 -1.041549  \n",
       "4       0.316566  1.549303                    5.484909 -0.020496  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数值型变量预处理，\n",
    "# 数据标准化\n",
    "# temp\tatemp\thum \t windspeed\tcasual(异常值,暂时删除)\tregistered\n",
    "# \"Plasma_glucose_concentration\",\"blood_pressure\",\n",
    "# \"Triceps_skin_fold_thickness\",\"serum_insulin\",\n",
    "# \"BMI\",\"Diabetes_pedigree_function\",\"Age\",\n",
    "# \"Target\"\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "mn_X = StandardScaler()\n",
    "mn_y = StandardScaler()\n",
    "numerical_features = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI','Diabetes_pedigree_function','Age']\n",
    "temp = mn_X.fit_transform(numericTable)\n",
    "\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index = numericTable.index)\n",
    "X_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants_0</th>\n",
       "      <th>pregnants_1</th>\n",
       "      <th>pregnants_2</th>\n",
       "      <th>pregnants_3</th>\n",
       "      <th>pregnants_4</th>\n",
       "      <th>pregnants_5</th>\n",
       "      <th>pregnants_6</th>\n",
       "      <th>pregnants_7</th>\n",
       "      <th>pregnants_8</th>\n",
       "      <th>pregnants_9</th>\n",
       "      <th>...</th>\n",
       "      <th>pregnants_15</th>\n",
       "      <th>pregnants_17</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.016662</td>\n",
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       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_0  pregnants_1  pregnants_2  pregnants_3  pregnants_4  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            1            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            1            0            0            0   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   pregnants_5  pregnants_6  pregnants_7  pregnants_8  pregnants_9   ...    \\\n",
       "0            0            1            0            0            0   ...     \n",
       "1            0            0            0            0            0   ...     \n",
       "2            0            0            0            1            0   ...     \n",
       "3            0            0            0            0            0   ...     \n",
       "4            0            0            0            0            0   ...     \n",
       "\n",
       "   pregnants_15  pregnants_17  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0             0             0                      0.866045       -0.031990   \n",
       "1             0             0                     -1.205066       -0.528319   \n",
       "2             0             0                      2.016662       -0.693761   \n",
       "3             0             0                     -1.073567       -0.528319   \n",
       "4             0             0                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并x and y\n",
    "X_train = pd.concat([X_train_cat, X_train_num], axis = 1, ignore_index=False)\n",
    "X_train['Target'] = y_train\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'pregnants_0', u'pregnants_1', u'pregnants_2', u'pregnants_3',\n",
      "       u'pregnants_4', u'pregnants_5', u'pregnants_6', u'pregnants_7',\n",
      "       u'pregnants_8', u'pregnants_9', u'pregnants_10', u'pregnants_11',\n",
      "       u'pregnants_12', u'pregnants_13', u'pregnants_14', u'pregnants_15',\n",
      "       u'pregnants_17', u'Plasma_glucose_concentration', u'blood_pressure',\n",
      "       u'Triceps_skin_fold_thickness', u'serum_insulin', u'BMI',\n",
      "       u'Diabetes_pedigree_function', u'Age', u'Target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "feat_names = X_train.columns\n",
    "print feat_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "#存为csv格式\n",
    "X_train = pd.DataFrame(columns = feat_names, data = X_train)\n",
    "X_train.to_csv('TrainData_pima-indians-diabetes.csv',index = False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#数据读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "      <th></th>\n",
       "      <th>pregnants_0</th>\n",
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       "      <th>pregnants_2</th>\n",
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       "      <th>pregnants_9</th>\n",
       "      <th>...</th>\n",
       "      <th>pregnants_15</th>\n",
       "      <th>pregnants_17</th>\n",
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       "      <th>blood_pressure</th>\n",
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       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
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       "  </thead>\n",
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       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants_0  pregnants_1  pregnants_2  pregnants_3  pregnants_4  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            1            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            1            0            0            0   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   pregnants_5  pregnants_6  pregnants_7  pregnants_8  pregnants_9   ...    \\\n",
       "0            0            1            0            0            0   ...     \n",
       "1            0            0            0            0            0   ...     \n",
       "2            0            0            0            1            0   ...     \n",
       "3            0            0            0            0            0   ...     \n",
       "4            0            0            0            0            0   ...     \n",
       "\n",
       "   pregnants_15  pregnants_17  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0             0             0                      0.866045       -0.031990   \n",
       "1             0             0                     -1.205066       -0.528319   \n",
       "2             0             0                      2.016662       -0.693761   \n",
       "3             0             0                     -1.073567       -0.528319   \n",
       "4             0             0                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"TrainData_pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['Target']   \n",
    "X_train = train.drop([\"Target\"], axis=1)\n",
    "\n",
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# trainingData for training \n",
    "#\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_train, y_train, test_size=0.2, random_state = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('logloss of each fold is: ', array([ 0.47509369,  0.48927645,  0.48793245,  0.53407648,  0.51908257]))\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "bad operand type for unary -: 'builtin_function_or_method'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-310-6e7dd723f90e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#%timeit loss_sparse = cross_val_score(lr, X_train_sparse, y_train, cv=3, scoring='neg_log_loss')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'logloss of each fold is: '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'cv logloss is:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'builtin_function_or_method'"
     ]
    }
   ],
   "source": [
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "#数据集比较大，采用3折交叉验证\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, Y_train, cv=5, scoring='neg_log_loss')\n",
    "#%timeit loss_sparse = cross_val_score(lr, X_train_sparse, y_train, cv=3, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', -loss.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [ 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=4,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters = {'penalty':('l1','l2'), \n",
    "#               'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#              }\n",
    "\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "lr_penalty= LogisticRegression(solver='liblinear')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss',n_jobs = 4,)\n",
    "grid.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.21096692281e-05\n",
      "{'penalty': 'l1', 'C': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xtykzHDgIzHFuaK7j16QxwV06A9BhvZmcf/byze5vXBuE3oChtnV8qXa6Nfy2\n46hr2xfiHrBo0SKmTp3KO++8g4+PT6HqevXVVylbtiylS5cmNjaWMWPGUL9+faKiomjbti0xMTG3\nPO0F1mleBw0aRFxcHLVq1aJVq1YcPny4UPHcjitPSUUC6ZqmXT/e99Xj0SjgwPU7552Kehl4AHjW\nngYzMzPtKUZWVtYNPx0t4LXXuLJuPRw/Tv+FJgaHfUvDsIbElYhzaDt36ofywHOw8T+EKpc5unEB\nmfVed2jbjubs98SV7pW+FLYfkQvmk2vjKancU6c4/XIfAEpN/gZDRITN7RpKlSLLbIbrPh9u15fr\n11//efLrr7/ywQcf8MILL/DMM8/c9bPmdvXk5OTg7e2Nn5/ftfWNGzdm27ZtjBkzhmPHjnHgwAES\nExNp1qwZmZmZ5ObmYrFYyMzMvDbTX6lSpa6VNxqNmEwmp/z7cmXC8AHy/1Wv9sjr+pV5p6J+AN7R\nNO20qqp2NZiQkGBXuasOHjxYqPJ3ouvZE68PPiAy2cJzK3MY7DuYD2I+wFPn+FNDt+tHpH91Sl35\nm0ZXlrBw3cNUCHbhaTE7OfM9cbV7pS+u7IeSmop33usjqalY7Ll2cIcy+fty7tw5AA4dOnTtw3nR\nokXMmDGDp59+mieeeKJAnzMnTpwA4MCBAzdMp5qUlIRer7+hjl9++YXFixfTtGlTVFWlZcuWfPHF\nF5w9e5aEhATOnj1LRkYGCQkJHD169FrcRqMRsF6ct1gsTnlfXJkwMsiXGK77Pf8E2O8BiZqmFeo2\norg4+76xZ2VlcfDgQWJjY/Hyyh+yg8TFcTEpkUvffkfLbRa2VUxiVdlVvFHr1jNr2eNu/dCZX4JF\nr9NMt5NPLqTTprHjz3k6ikveExe5V/rijn7kBASQmPe6YsWKeEQ7ZhKl2/UlKe8aS0xMDBUrVmT+\n/PnMmDGDfv368fLLLxe4fs+86yUVK1YkMjLy2vqDBw9iMBhu+Kzq3bs37777Lu3ypn3Oysriww8/\nJDQ0lLi4ONasWYO3tzdxcXGkp1s/OqtUqXLttFh8fDyA3e/LnRKgKxNGIuCrqqq/pmlX8tZdPZ7M\nf+XreSBCVdWrjyJ7AB55v7fQNG1tQRq8mnHt5eXlVeg67qTUa6+RsX49WXv28urvZt4pNZPHyj9G\nvVL1HNrObftR41mylwzG05SG175fUDo8gpfhrnc5u5Wz3xNXulf64sp+6K77APTy8sLTwe3m78vV\nD1wvLy+uXLnCuHHjaN26NZ07d+bKlSvX9gsMDLyWFG5X763q9/CwHtVfvy48PJwNGzbQqFEjUlNT\nmThxIikpKZjNZoxGIwaDAUX49SSRAAAgAElEQVRRMBqN19q8vl6DwXDLthzBlRe9d2E9kmhy3bom\nwBlN0w7l27cpUA3rhfKaWG/HPZn3epvTI3URxdOTyPHjUTw9KXkFeiwz8d7690jLSXNNAJ6+5FSx\nPjHaxhTP8oQzrmlXiGJo1apVZGRk8Pvvv9O4ceMblvXr1zusnbFjx5KYmEibNm3o168fUVFRPPvs\ns4U+xe4IiivnaFBV9XOgNdANMAI/AZ9rmjZGVdUQAE3TbprdR1XV/sDbmqaVK2hb27dvt9SpU8eu\nODMzM0lISCAuLs4l35wuTJ3K2bHjAPi0nY6oNh15v9H7ha63QP1I3AbfNQfgw/D/8F5f+25VdDZX\nvyfOdK/0xR39yD5+nENPWB88jVm2FM8yZRxS773ynkDh+7J9+3bq1Kmj3GpbgY8wVFX1UVX1A1VV\nK+b9/p2qqmmqqsarqlq6gNW8CywH/gBmYX1wb1zetnl5y30n5IUX8KlfH4CXlphZsXMuaxLXuKbx\nyDqk+Fvvaq50cgGnLme4pl0h7OBZpgxV9u2lyr69DksWouBsOSU1Aeu1BU9VVTvmvR6A9WL25wWp\nQNO0TE3TXtY0LVDTtDBN0969+sCepmlNNU1reptyX9hydFHcKDodpceMRufrS0CGdcKl99eP4HLW\n5bsXLnTjCt4NugPQSreJhVsP3Hl/IcR9y5aE8QzwnKZpCUB7YImmad8BbwM3P34obOIRGUn40KEA\n1D5k4YFNZ/lo00euabtmZ0zo8VMySd7ys1unkhVCFF22JAxv4FzeYIFPAkuuq8P1w67egwLbtcXv\nMev1hBeXm9mx8w+WHF1yl1IO4BdKarknAGieuZStRy86v00hRLFjS8LYCozAes0hAPhNVdXyeb+v\nc0Js9x1FUYgYORJ9iRIYc6Df7yZGbxjF+Qznjygb0LA7APV0+1nlwDs+hBD3DlsSRj+gFtAbeEvT\ntJPA61jHgnrNCbHdlwwhIUR8OBKAyonw8JqLvL/hfaefJlJiHyPNKxSAkAM/k5aV69T2hBDFT4ET\nhqZpezVNq6lpWpCmaRPyVg/UNK2+pmnHnBTffcm/WTMCO1ifj+i0xszR7atYcHCBcxvVG1BqWgck\nfIbV/LFL5ksWQtzIlttqFVVVe6iqGp33+zBgp6qq36uq6u+0CO9T4YMH4xEZicEM/Rea+HTjWE6m\n2jf+f0H55E3fGqpc5vCGX53alhCi+LHllNRY4BMgTFXVJ7Bez5iP9YnsCXcqKGyn9/Oj9NgxoCiU\nPQet46/w3vr3MFucOKVqiRguhlqHJal1YSFHzrvoiXMhRLFgS8J4HnhW07TtwHPAKk3ThgF9sN5y\nKxzMp149QnpYn7xus8lCytbNzNo3y6ltBjS0ttdMt5PFG/9yaltCiOLFloQRjHUyI4AWwKK816m4\nfqrX+0bogNfxqlgRHda7piZt+Iyjl486rT19tbZk6XwwKGZMf/0Xk1meyRBCWNmSMP4GXlFVdQAQ\nDixUVdUXGAxsd0Zwwjo6Z+nx48BgIPwSdFqWztB1Q8k1O+kuJk9fMvPm+G6Rs5y1+886px0hRLFj\nS8J4C+vpp38Dn+WNMDsOeAL4lxNiE3mMVaoQ+pr1zuXmuyx4bPyLqQlTndZeYKOeAMTqTrJt3VKn\ntSOEKF5sua12HdZnLkpqmvZW3uqRQDlN03Y7IzjxPyV69cS7Zk0A+vxhZtr6L9CSNec0FlmHy37W\nAQnLHp/HpfRs57QjhChWbJ0PIxR4U1XVeaqq/gq8AciQkS6gGAyUHjcWxdtIUDr0XJzNkLWDyTY5\n4cNcUfDKu8W2hbKRRdvujalEhRCFY8tzGPWB/UA74DxwDnga2K2qal3nhCeu51m2LOEDBwLQQLMQ\nvlbj611fO6UtY+0u1wYkPLv5v05pQwhRvNhyhPEp1jksqucNUd5b07Q4YBrwsVOiEzcJ6tQJ34et\nkxb2+tPMr+unsOvcLsc35BfKpTKPAfDQlSUknHTBUOtCiCLNloRRF+vF7vz3WU4AHDsJtbgtRVGI\nGDUKXWAgPlnQ9/dc3lszlIxcx098FJx38bu+TiN+nQxIKMT9zpaEcQood4v15YErt1gvnMQjLIyI\n90cAUO2Yhcorj/Cf7f9xeDu6io+R6lkSAL89/yUrV0axF+J+ZkvC+BGYrKpqO1VVI/KWDsA3WKda\nFS4U0KIFAa1bA9B1lZnV62aw+dRmxzaiN2B+oDMArSyriU9w7lhWQoiizZaEMRrrpEk/A4lAEjAT\nmAMMdXxo4m5KvTcMfXgYnrnWAQpHrBnGlWzHHuxdHSokTLmEtu6+nHJdCJHHlucwcjRN6wOUBB4E\nagBBmqa9rWlajrMCFLenDwyk9OjRAMSchkbLkhi/dbxjGykRw/kS1pvgqp7+ldOXMx1bvxCi2Ljj\nGFCqqra8S/loVVUB0DTtD0cFJQrO76GHCO7alYszZtB+g4VhsfNZVaY5TaObOqyNgEY9YOE2mul2\nMn3TX/R48kGH1S2EKD7uNmjg7wWsxwLoCxmLsFPY22+Run49HD3KawtNfBQ1ghodF+CNt0Pq96ze\njsxF72A0p5O9fSaWJxqgKIpD6hZCFB93TBiaptn6JLhwA523N5Hjx3G0c2dKJ5tpseQco6JHMarB\nKMc04OlLWsVnMGqzaJ75J9uPJlO3fAnH1C2EKDYKPCy5qqq3GwLEAmQD5zRNc+LsPuJOvB94gJJ9\nXuH8V1/RYruFDysuof6xZQDMLzufWGNsoeoPadwLtFnE6k7y5dql1C3fxRFhCyGKEVuOIA4BR/KW\no3nL1dcngTRVVX/KG/JcuEHJvq/gFRcHwKuLzPhmOG4uCyWqLhd9KwAQfuhn0rOdNLy6EKLIsiVh\n9MaaNFoCQXnLk4AGDAIeBqKRYULcRvHwIHL8OPD0oMQV6LnMesBnsTggcSgKHvWsAxI+xUaW7jxU\n+DqFEMWKLQljJNBL07Slmqal5C3LsSaS/pqmbQXeBDo4I1BRMF4xMYS/ZR19vskeCw33mll8bLFD\n6var2/XagISnNsiAhELcb2xJGEHcegiQTODqFdBkwKewQYnCCe7WDV1d69wZLy0xs2Dz9yRcSCh8\nxX6hnI9sBkDdi4s4diGt8HUKIYoNWxLGEuAbVVXjrq5QVbUa8BWwRFVVD+BVwAlDpwpbKDodni91\nwwL4ZUK/BVm8u+IthzwFXqJJL8A6IOEKGZBQiPuKLQmjD3AR+FtV1XRVVTOwJofTedueAl7EOpWr\ncDNDrep81tb69lY9AU3+OM6IDSMKfT3DUPFxrnhYByQ07J6Jyey4C+tCiKLNlqFBLmma9hSgAt2A\nTkAlTdPaapp2AVgOhGua5uAR8IQ9ogOimfj+dnKefAKAZzZZuPjnUmbtm1W4ivUGcqo9B8BTuStZ\nv/90YUMVQhQTNj2Yp6qqEevdUA8BzYHHVFUNAtA0LeMWc2UIN8vp0gXP6tUA6Pe7mR//HE/C+cJd\nzwhpbJ0nI0y5xJ41vxQ6RiFE8WDLFK2VgH3AWKAaEAeMAvaoqlq4p8KE8xgMhI8fjxIYgG8WvD4v\ni4HL3yQlO8X+OkvEcCa4DgAxiQu4lO6EecWFEEWOLUcYE4CdQFlN057QNO0xrBMqbQQ+c0JswkEM\nERFEffIJFkWhwml44tcTjFhfuOsZAY2sw543VXawbMvfjgpVCFGE2ZIwHgaGaZqWfnWFpmlpwPvA\nIw6OSziYX5MmlHylDwCP77SQ9ccyZu6baXd93jXak6nzwUMxkbrlJ0eFKYQowmxJGMlA4C3WBwIy\nH0YxENq/Pz4PNgCsz2fMWjyef87/Y19lnr5cqtAGgIdTl7D35GVHhSmEKKJsSRjzgK9VVa11dYWq\nqnWAL/O2iSJO0euJ/PRTdGGhGHPgX3OzGbrM/usZYQ/3BiBWd5KNq5c4MlQhRBFkS8IYivWZi+2q\nqmbkPYexFTiAdUgQUQwYSpQg+rPPsOh1RCZDmzmJDF/3nl3XM3TR9bjgXR6A4P2zyc6VwYqFuJfd\nMWGoqlr16gKUAd4AqmOd3/udvNfDsQ46KIoJnzp1CH/T+nzlQ3st6BcsZ8beGbZXpCjo67wAwOPm\n9az+57AjwxRCFDF3mw/jH6zzXVydXu3q19Drf1eQGfeKnZCePUjfsYPUFSvovtzM+5EfUyO0BtVD\nq9tUT9CD3chdNwo/JZMTa2dBzfecFLEQwt3udkqqPFAh7+fV1/l/v/pTFCOKolB6zGj0UZEYzPD6\nvGyGL36Dy1k2Xrz2C+VsxKMAVD+3kLMpmU6IVghRFNxtitZjrgpEuJ4+IIDoCRM40rkzYZdz6DD7\nJO+VHsaE5p/bNGd3iSa94efl1NNpzFq/ns4tmjsxaiGEu8ic3fc577g4IoYOA6DOIQuBc+KZvme6\nTXV4qY+TYsgb4X7nT46ZsEkIUeRIwhAE/V9HAp5+GoDn1phZMu8Tdp/bXfAK9AbSq3YCoHlWPDuO\nnndGmEIIN5OEIVAUhYj3R2CIqYDOAq8tyGHk77Zdzwh/2DpPRphyiX9WzXVWqEIIN5KEIQDQ+fhQ\nZuJE8DESlAadZ57kvdVDCnx6SSkZy6lA6zOdUUd/IT0715nhCiHcQBKGuMarQgVKfzgKsE66VGrW\nSqbtmVbg8r4NrQMSPswOVm6zc8gRIUSRJQlD3CCwVSuCunYBoN1GC2v++yl/nf2rQGUDaj9LhmId\nkPDiJtsunAshij5JGOIm4QMH4lmtKgCvLMxhzIICXs/w9OV8udYAPHjpD46fT3NmmEIIF5OEIW6i\n8/SkzITPwd8Pv0zoNvM0760cjNly97GiIpr+b0DC9av/cHaoQggXcmnCUFXVQ1XVL1RVvZC3jFdV\n9ZZDiqiqGqWq6hxVVc+rqnpaVdXvVVUNdmW89zOPyEiiP/kEgJjTUOHHVUxLuPv1DEOZ+pwzWgck\n9E2Yhcksz2QIca9w9RHGGOBJoBXQCegGDM6/U14S+RUIAJoBTwM1gIJfgRWF5vfII5To8zIAT+60\nsGV6Aa5nKAqWWs8D0My0nk37ZLAAIe4VLksYqqoagb7AW5qmbdI0bTkwCHhNVdX8cdQEagMvapq2\nW9O0LcDrQGtVVYNcFbOA0Ndew6t+XQBe+iOXT+YM4FLmpTuWCXvoRXLR46dkcmSN/bP6CSGKlruN\nVutINQEfYPV169YAYUAM1nk1rjoKtNA07fR1666e2wgC7vyJlScz076B8LKysm74WVw5qh9ho8dw\nvNP/YbxwkRdnnmV4mXcY+9gEdMptvm8Y/DkT+jBlz62k8skFnLn4GoHeHoWK4V55T+De6cu90g+Q\nvhSU4qpxf1RV7QBM0zTN97p13kA60EzTtJV3KT8HqKlpWsWCtLd9+3Y5ee5Aur378Bz9ETqzhbVx\nCmd6P0fLsFa33d8rcQPVdlrHqPo65hvqVy3Q2yaEKALq1Klzy9FHXXmE4QPk/8p/NQV63amgqqoD\ngfZAa1sajIuLs2X3/wWVlcXBgweJjY3Fy+uOoRVpDu1HXByXrqRwccLnNEmwMGXpHHLfepIaoTVu\nvX8VlUu7PyPIdIHQpD+J69i2UM3fK+8J3Dt9uVf6AdKX6yUkJNx2mysTRgY3J4arv6ffrpCqqu8B\nI4H+mqYttqVBo9FoU4D5eXl5FbqOosBR/Qjv04fMXbvIWLWaF/7M5bNyb/PvV34j2Hjrm9fOqR0J\n2jOJhzOWcyw5A7V04W9yu1feE7h3+nKv9AOkL3fjyrukEgFfVVX9r1sXkfcz6VYFVFX9D/AB0FfT\ntC+dHJ+4C0WnI3rcOIgIw8ME3Wed54Nl79z2+YzIR18CrAMS/hX/sytDFUI4gSsTxi6sRxJNrlvX\nBDijadqh/DurqjoSeA3ooWnaJNeEKO5GHxhIuYlfYTboCbsMdSdv4PvdU265ry40lsQA64CEYQfn\nkmO6+4N/Qoiiy2UJQ9O0DGAK8IWqqg+pqtocGAtMAFBVNURV1ZC817WAocAnwFJVVUtdt7jyNJq4\nBe9qcUQMy5t06aCFY5MmsP3M9lvv26A7AI0t21n31x5XhSiEcAJXP7j3LrAc+AOYBUwHxuVtm5e3\nAHTIi+1d4FS+pbIL4xW3EdypE76tWgDQaZWJyT8OIDkz+ab9StTrSHregITn1v/o6jCFEA7k0m/r\nmqZlAi/nLfm3Nb3u9TBgmOsiE7ZSFIWokR+i7fkH/ZETvDj7AqNi3uaT9t/d+HyGpy9nyrSk/LG5\n1LqwiLMpIwkL8HZf4EIIu8ngg8JuOl9fKnzxNWajJ8Fp0HDyJr7f9d1N+0U0tV78rqgksWG1TTe6\nCSGKEEkYolC8YmKIGjUagGrHLJz9fALbTm+7YR9juQac8SoLgOfuWQWexU8IUbRIwhCFFti6FX6d\nOgLQboOZad8O4ELGhf/toChkP2AdkPDh7DXsPnzLu6iFEEWcJAzhEJFDh6FUsQ7/0e2XZMb89uYN\nz2dEPdL92oCE+1fOcFeYQohCkIQhHELn6UmFiV9j8vPGLxMembSFKTv+9/iM4hfG8ZIPA1DhxDwy\nsk3uClUIYSdJGMJhPKMiKfvxpwDEnoK0f3/J1tNbr20PadwTgDrKPtZt3uiWGIUQ9pOEIRzK/9FH\nCezVA4Ant5v5+cvXOZ9xHoCg6i25pC8BQMZmeSZDiOJGEoZwuIg33kSpVQ2ALgsu8fGcAZjMJtAb\nSI7tAMCDV5Zx4nyKO8MUQthIEoZwOMVgIPbzr8gN9seYA80n72DK1q8AiGpufWYzTLnE9hVz3Bmm\nEMJGkjCEUxhCQ6nwny8wKxB9Hvh4EltObsYzrCLH/GoCEKzNxmyWZzKEKC4kYQin8W1Qn5ABrwHQ\n5B8zv/3Hej3Do94LADQybWNbgubOEIUQNpCEIZyq1MuvoGtcH4COi1L498z+hDfoSDreeCgmTq35\nwc0RCiEKShKGcCpFpyP2kwnkhAXjaYInvt3F99unkBRpHek27uxCLqdnuzlKIURBSMIQTqcPCqLi\nl5MxGXSEXwKvsd+SVNM6j1asksSmNUvcHKEQoiAkYQiX8K5ejbBBAwGod8DCuulfk2AsA4Dy10/u\nDE0IUUCSMITLhHbthu6JRwBo+2cq0yxBmIBGGas5kHjavcEJIe5KEoZwGUVRqDjmU7Kjw9Bb4Olf\nzjPFMwg/JZN9K6a7OzwhxF1IwhAupfP1pfLXU8j11BOSCkGrfdnk6UXkkV/IMZnvXoEQwm0kYQiX\n84qNJWLkSACqH7Ow7kAJovUaW7ZtcXNkQog7kYQh3KJk2/bo27cEoNVGmJQaysWN8kyGEEWZJAzh\nNrEjRpMVGwXAk0v1HL6yknOX09wclRDidiRhCLfReXlRddIPZHsb8M+Eciv1zF401t1hCSFuQxKG\ncCvPqCiix38CQMWTYF40jzNpZ9wclRDiViRhCLcLefxJMptXB+Dx7WYmffIiueZcN0clhMhPEoYo\nEmqMn0x6Kes8321+OcaPi0e7OSIhRH6SMESRoPMNwrtNHTK9LXhnQ9TYWWw4tNLdYQkhriMJQxQZ\npVr1o3zDC5gVC2XOwb4hb3A6VYYMEaKokIQhiowQ9SHSIkvhVcN6a23DXVnM+LiHXM8QooiQhCGK\nDkUhpfJzxKgppEZZk8Rjc48ybcFINwcmhABJGKKIiWnei1xFT61650gJ8MDTBOXHz2HTwXh3hybE\nfU8ShihSPINKcTDoIQxeFvQPepKrVyh1CZKGDSY5+4K7wxPiviYJQxQ5vg16AFA35ACXO7cFoPa+\nbNb+8Aaz9swgPSfdneEJcd+ShCGKnDL1n+aCEgJAZsBFLjaOA6DzGjNVen/ClB4P8umU3qw+tpIc\nU447QxXiviIJQxQ9egMny1mPLB64sJiqYyaRUUsFIDAdHtuWQ8uP16N/9lW+6l2fiTMGsO3UVswW\nmU9DCGeShCGKpLLNXwYgTLnIrk2LqfrDf8n49BO8+vQko0wYACWuwBMbM3nsw2VcafcCn/dpwOT5\nQ9l7YS8Wi8Wd4QtxT5KEIYqkgKgqHPR+AACvf2YCYClVitJ9X6P2stWU/+1XPHt1JaNUEAClLsGT\na1JpMngeJ55pz4TXGjN1yRiOpRxzWx+EuNdIwhBFVu4DXQCok7mZo8eP3rDNWKkSMe8Mo9bKDZSb\nOxelSzsySvgBEH0enlyeTIN/TeOfp59iwtvN+O+qiZxNP+vqLghxT5GEIYqsio92Iw0jHoqJE6un\n3XIfRVHwrhZH5eGjqbV2M2Vm/ISp/ZNkBhgBqHAanvj9FDVe+Yr1Tzfli6GtWLB5KpezLruyK0Lc\nEyRhiCJLb/TjYNiTAMQk/Uqu6c4XtRWdDt86dag2+j/U3LCVyCnfktWyCVk+HgBUTrTQ/JfDVOw+\njmXPNOLrD9uzbPcvZORmOL0vQtwLJGGIIi2sSS8AKpBI0qHdBS6nGAwEPNSYmv+eTI1N2wj/8nPS\nmtcl20uPzgLVjpppOmMvEc8NY0G7ekz++HnW7l9Gjllu0xXidgzuDkCIO4mo9jCJC8oQZTqO/5E/\nyM7tjNHGOhRPT0KaP05I88cxZ2ZybsVSjs37Ce8tezDkmKl5wAQHtpP943ZmV/Qkp1kDHnimB7Wi\nG6BT5DuVEFdJwhBFm6Jwolw7og5NoHnWCvaPr89m78pkhNXEt1x9ylatR4XwIHQ6pUDV6YxGwls9\nQ3irZzClpnFq6a8kzpuF386DeOZCnb3ZsHctGd+u5acqPiiPNabu072pHFYNRSlYG0LcqyRhiCKv\nRutXSfn8ewK4QmXlBJWzTsCJP+EEZK7x4G/Kcdq/KjnhtQis2IBKVWoSHuhz13r1fr5EdehCVIcu\nmC5d4tjCnznz2y8E/HMc72yotysddi0j9ctlTKsegPGJx2jY6iXKhJRzfqeFKIIkYYgizye4FBmv\nb2XHqjn4ZiRiOLOLsJQE/C1XMCo51OAANVIPQOqvcAhSFvuwVRfL+cA4iKxNaKWGqJVU/L09b9uG\nPiiICt1epkK3l8k9d46D838i+fffCN5/Gr9MqL81BbbOI+mzeaysVZKAli156MmehPmFu/AvIYR7\nScIQxYLiHYylzEOUjYvDaDSCxYIp+Shn9m3gyqHNeJ75i4i0fRjJIkBJp55lN1zaDZdmQQKctQTx\nt0clLodUx6NMPUpXbkRsuWg8DTdfozCEhlL55Tfg5TfITkpCm/sDVxYvJvhoMkHpUH/9eVg/DW3c\nNJbULU3JNu14qFk3Ar0C3fCXEcJ1JGGI4klR0JcoT+mHysNDXa3rTLlknd7LqT3ryTi6Fb/zu4nI\nOoQBE2HKJcJyt8DZLXB2CmyDY5Zwjhkrk16yBj7l61EmriFlS5W84VqFZ2Qk1QcMgwHDSD98iL1z\nviN76QqCTl6hZAqUjD8J8V+yI/grkh4sT+lnOtKo8XMYDbZemhei6JOEIe4degNekdUpF1kdeMW6\nLieTlKM7OLNvA7nHtxF08R8ick8AUFY5Q9msM5C0GpIgd62O/Uo0J32rkhNek8CYBlSIq0dokPUJ\ncp8KMdQZOAYGQsref9gz+1tYsZbAcxmUumih1OLDsHgca8M+5nyjypRt35X6dZ/GoJP/ZuLeIP+S\nxb3Nw0hAxUYEVGx0bZUl4yLn9m/hwv5NKCd3UPLyP5Q0n8egmFE5hpp2DA4vhsOQucyDv3UVOBcY\nB6VrU6LSg8RWrkFAlWo8+P4ELCMsXPhrC/tmf4fnqi34X8om6qyZqAV7YMFQ/iw9gpQmD1Dp2R7U\nqNZMbtMVxZokDHHfUbyDCavxJGE1nry2LvfSSU7u2UDK4c14nP6L0ml78LekYlRyqG7R4JIGl+bB\nHrhs8WGHRyUuBVXDUKYu4VUa8eBH36BX4PSmVeyf8wM+a//CLzWXcidzYfYOmL2DP8p6kdW0LpXa\ndsds8XLjX0AI+7g0Yaiq6gF8BnTOWzUFGKxpmqkw+wpRWIag0pRp9Cw0eta6wmIh4+xBkhI2kHFk\nM97ndxOVoWEkm0Alndq5f8H5v+D8T7ADzliCOWasTFqJBzC2fJrwN0Zj2r+do/NmELhhD96ZZmKO\nZcGP6zFPW0+2L6zz1pPtbSDHxwOTjxdmX2/w80Xx88UQEIghIBDPwGCMgSF4B5fELzgM/5Bw/ANC\n8fP0k6MV4XKuPsIYAzwJtAL8gOlACjCqkPsK4ViKgnd4RWLDKwIvWteZcrl4fDen92wg5/g2ApL/\nJirnMAbMhCsXCc/aCCc3wslvYD0cIwJjuSpcrtuO8ykWLH/9RfiOI3hlWwhJBVJNgAnIAlLvGpIF\n63+AZB2ke0GGUXct4eT6eGH2NWLx9UHx90Pv75+XcILwCgzBJ7gkPkGh1oQTHI6/d5AkHGEzlyUM\nVVWNQF+gs6Zpm/LWDQLGq6o6WtM0sz37CuEyegPB5WsTXL72tVWW7HROaVs5v38jlqTtlLicQKQp\nCYCynKJs+ik4Hg9AblkdB6OjOJQaRnqGBZ0FlGwzSnYu+qxc9FkmDFm5eGaZ8Mw0Y8wyY8y03DTg\nm8EMARkQkGGGi9lANpBWoC5cyVsyPCHDSyHL20C2jwe5Pp6YfI1YfL2tCcfPH31AAB4BgRgDS2AM\nLoFvUCi+IWH4B5fC379Eof+covhx5RFGTcAHWH3dujVAGBADHLBzXyHcRvH0IaL6I0RUf+Taupy0\niyQmrOfSgc14nN5JeOoeQi0XMChmKuuPUznwOBTwkQ2L5f/bu/MgOcoyjuPfnk2ymwSEAAIRVITI\nUxATjhAphHAoh0gKEcuyIAZFShTkUI4ICGoBliBQHIJSahS0ECwEBeRSLDCAhmOhECI+ECFSCYKS\nBAwku3O1f7w9Se+ws+mZHWZ2Nr9P1VbPvP1O9/MmNf30293zvlAqRvQVcqwqdvFWsYvVxS76ijny\nhRyFQo5SPke5EEE+IpfP0ZWHsfmIcf3Q0x8xdpCLuOPzMD4fw6oCUABWZ4rnreRvadLL6euOWJiL\niCNSfxHlCEhep8srS/r9usYAAAqYSURBVFJlRBDnolR5lPpsBFFEnCMso8oyglxYt+4zoTxa+5nK\n+nTdCKLcurJc8h7I5/M819NNjtQQMFGN14OpuT4abBHaAkTVKyrrq4uG2n9qXRzDhGn7MnXq1KHj\nbUArE8Y2wGp3T09E8Eqy3JaBSaCeujX19fU1FGh/f/+AZacaLe2ADmtL13gmTz+QydMPXFu0YvlS\nXn52IWv+9Tg9ry0il1/FuFyZXFxiTFwgFxcZExfporIsMZYCY6IS48YW2WhsiY1o7PZduQRrCjlW\nlbp4s9jF6kIl4XSRL+Yo5iPiQo44HxHlI7ryEWNSCWd8/u3bXNfLiQmHNhlJVv7xaVYffNT6K9ap\nlQljAlB9BK98+6sfGamnbk2LFi3KHNxgFi9ePKzPjxSjpR3Q4W2ZtBO5STtROf5mPp2JY4jLROU8\n5VKRUrFAXCpSLhXC+1KBuFQiLuUpl0uQlFMqQLlIXCqErFEO7yt/ueR9V1wkKhfJlYtEcZFc8j6K\ni+TjsP1SoUCh0E++UKJUKFIqFinly5SLccgXcQgTIIrjta+r1xFDVLUu5Js46XIkdYgHrI9SeSka\npDyi6n1qPwPLwj5y6bLquBIDzueHWjfE+kpxrW3XWl9tqP0N1u94acp4upcsGXqjDWhlwljD2w/2\nlffV/eF66tbUaJesv7+fxYsXM2XKFLq7O/fxx9HSDlBbRqLR0g4YfW0ZN4y2DHWi3cqEsRSYaGYb\nu/uqpGxyslw2jLo19fQMb3iG7u7uYW9jJBgt7QC1ZSQaLe0AtWV9Wvlc3VOE3sGsVNks4FV3/+cw\n6oqISAu0rIfh7mvMbD5wtZnNBXqAi4ArAcxss6TeivXVFRGR1mv1D/fmEQ7+dxFuYl8HXJysuzVZ\n7p+hroiItFhLE4a79wHHJ3/V6/bPWldERFpPYwOIiEgmShgiIpKJEoaIiGQSxfF6fmLYoXp7e0dn\nw0RE3mEzZswYdOCqUZswRESkuXRJSkREMlHCEBGRTJQwREQkEyUMERHJRAlDREQyUcIQEZFMlDBE\nRCQTJQwREcmk1cObdwwzGwNcCBwDbAIsAL7m7t7WwBpgZjngTMLIv1sBTwNnuPvDbQ1smMzsKOB0\nd9+j3bFkYWZjgcuBo5Ki+cDZ7l5qX1TDY2YRYQqCO9396nbH0wgz25bw/3IAUCS053R3X9nWwOpk\nZjsCVwF7A28C1wPnuXuhWftQD6O2bwNHA3OA3YDXgbuTRNJpTiEkjNMIbXkQuNfM3t/WqIbBzA4C\nftruOOr0PeAQ4DDgs8Bc4Oy2RjQMZtYF/BD4eLtjaVTShtuAdwEfBQ4HdgF+0c646pWcjNwDrARm\nEE5K5gDfauZ+lDBq6wJOdff73f05wpf9A8D72htWQ44DLnX329z9eXefB7wMfKrNcTXEzC4Bfg90\nzHS9ZtYDnEA4c13o7vcBZwEnJz3AjmJm2wN/Bg4lnEx1ql2B3YHPu/vf3P1RwgnWbDPbtL2h1WUb\n4DHgy+7+nLs/ANwM7NfMnXTi2XJLuPs5lddmNgk4FXgOeKltQTXuJAY/uHbSFyLtY4SzwYOA2W2O\nJatdgQmEg2zFAmBLYAfg+XYENQx7Af8gnJE/0eZYhmMJcKi7v5IqqwywtykdkgzdfQmh1wqAme0C\nHEGYqbRplDDWw8zOBL4P9AGfcPdim0Oqm7unD1KY2WHAB4H72hPR8Lj77rD2slSn2AZY7e5vpMoq\nB6lt6bCE4e43ADcAmFmbo2mcuy8nXMpJ+zqwODkIdxwzWwTsDPQS7s00zQabMMxsHLUvL/3P3f+T\nvL4F+BPwJeAOM5vp7s+2Isas6mgLZrYz4WbYje7+UCviq0c9bekwEwgnHWn9ybK7xbFIDWb2DeBI\nOqfnOpi5wCTCDfDfEXrkTbHBJgxgR8LTQoO5HvgCgLu/AGBmJwKzCPcDzmhBfPXI1BYzmwHcDSwi\ntGMkytSWDrSGtyeGyvvVLY5FBmFm5wHnAye5+93tjqdR7v4EgJkdCzxiZh9y92ease0NNmEk/4CD\nThJiZjkzOxzodfdlSf3YzP4ObNHCMDMZqi0VZrYv4UbxI8An3X1NK2KrV5a2dKilwEQz29jdVyVl\nk5PlsjbFJAkzu4Jws/sEd7+23fHUy8zeA+zp7r9NFVeSRNOOWR33dEYruHsZuIbU2Wzy+N1uhLPz\njpLcALuTcJN1trvrjLb1niL0JGalymYBr7p7xzztNRqZ2fnAycCxnZgsEjsBt5jZe1NlM4Ey0LRL\n6BtsDyODq4BzzewZwIF5wETgx22NqjE/JzxG+1VgUuom5Vups115B7n7GjObD1xtZnOBHuAi4Mr2\nRrZhM7PdgG8ClxJ+m7R1avVrHfSQywLCScn1ZnYK8G7Csepad3+1WTtRwqjtMkIP7Apga+AvwIFV\nT7mMeGY2hdAzgvAIYdpljLz7MaPZPEKiuItww/s64OJ2BiR8mvA9n5f8pU1j3WWdEc3dC2Y2m3AC\n8iDhF+u/JPzWp2k0p7eIiGSiexgiIpKJEoaIiGSihCEiIpkoYYiISCZKGCIikokShoiIZKLfYYhk\nZGbbAS8C05oxNk8yesDDwDHJnCuY2VaEH5IdTpgd8SXCZD6Xunt/UucqwrA11w83BpF6qIch0j6n\nAE+lksV2hLkldiBMDbwzcA7wFeCm1OcuAM43s81bGq1s8JQwRNrAzLoJ07P+IFX8I8IwNIe7+wJ3\nf9HdbwE+AxxhZocAuPt/CUPun9TisGUDp0tSIg0ws42BCwlDS2xGGMvnVHf3ZP0k4FrCFKZvAOcR\n5iCfkpod7fXKpS0z24Zkvm93L6X35e4LzewA4PFU8a3AfDP7bgeNdyQdTj0Mkcb8hjBN7FHAnoTJ\nkf5gZhOS9TcC2wMHAJ8j3JfoSn1+NgNneptOGNb90cF25u4PuPubqaL7gM2BGcNuiUhGShgijTmY\nMBz2g+7+NDCHMJrxHDPbkdBbOM7de5Mpck+u+vxMBg6VPylZZhrc0t37gBeS7Yi0hBKGSP2OBPKE\nOZMBcPe3gCeBqYTeQj8DZw78a9U2tgJeS72vvJ5EdsuBLeuoLzIsShgi9Xu9RnmOcNmpwPpnDSwz\n8PvXm5R9eLDKZnaDmc2pKu4CSoPVF3knKGGI1O9+YBywR6XAzCYCuxBmN1uUrJ+W+kz1paNXSE2d\n6e7LCfNknGFmA76XZrY3cDSwsmobWyTbEWkJPSUlUr+Y8JTSz8zsREKP4zuESWtucvcVZnYH8JNk\nfTfrHp+tTEDTS0gwaacRfsh3u5ldBPwb2Ae4BLgZuLtS0cw2AbYDHmt240RqUQ9DpDFfJDzRdDvh\n/kQPsK+7r0itX0aY/ezXhNn1INz7gDDH+n7pDbr788BHCPcmbiLM9nYWYWbEue6enu1sH0JCebKZ\njRIZimbcE2my5NHaA4F7U8N5zAQeAia6ezGpswQ41N17a26s9j5+BTzr7hc0L3KRoamHIdJ8fcB8\n4Ltmtr2ZzSD0Em6t/MjO3VcTLjWdWO/GzWwysD9wTdMiFslACUOkydy9TBg8cC/Co7X3EG6GH19V\n9XJguplZnbs4Fzg3dflLpCV0SUpERDJRD0NERDJRwhARkUyUMEREJBMlDBERyUQJQ0REMvk/PCF/\navj9xs8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "# Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "#     plt(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(penalty = 'l1', C = 1000)\n",
    "lr.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "X has 24 features per sample; expecting 11",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-394-9dc2017fdd61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/DalinXie/anaconda2/lib/python2.7/site-packages/sklearn/base.pyc\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    347\u001b[0m         \"\"\"\n\u001b[1;32m    348\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/DalinXie/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/base.pyc\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mPredicted\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \"\"\"\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/DalinXie/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/base.pyc\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    303\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             raise ValueError(\"X has %d features per sample; expecting %d\"\n\u001b[0;32m--> 305\u001b[0;31m                              % (X.shape[1], n_features))\n\u001b[0m\u001b[1;32m    306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         scores = safe_sparse_dot(X, self.coef_.T,\n",
      "\u001b[0;31mValueError\u001b[0m: X has 24 features per sample; expecting 11"
     ]
    }
   ],
   "source": [
    "# score\n",
    "lr.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#\n",
    "# 没有处理pregnants\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>29</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data from file\n",
    "trainData = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "trainData.head()\n",
    "# print data shape of training Data\n",
    "# print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp = trainData['pregnants']\n",
    "trainData = trainData.drop([\"pregnants\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "trainData[NaN_col_names] = trainData[NaN_col_names].replace(0, np.NaN)\n",
    "print(trainData.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = trainData.median() \n",
    "trainData = trainData.fillna(medians)\n",
    "print(trainData.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>0.167</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose_concentration  blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "0                         148.0            72.0                         35.0   \n",
       "1                          85.0            66.0                         29.0   \n",
       "2                         183.0            64.0                         29.0   \n",
       "3                          89.0            66.0                         23.0   \n",
       "4                         137.0            40.0                         35.0   \n",
       "\n",
       "   serum_insulin   BMI  Diabetes_pedigree_function  Age  Target  pregnants  \n",
       "0          125.0  33.6                       0.627   50       1          6  \n",
       "1          125.0  26.6                       0.351   31       0          1  \n",
       "2          125.0  23.3                       0.672   32       1          8  \n",
       "3           94.0  28.1                       0.167   21       0          1  \n",
       "4          168.0  43.1                       2.288   33       1          0  "
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trainV1 = pd.concat([trainData, temp], axis = 1, ignore_index=False)\n",
    "X_trainV1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_trainV1 = X_trainV1['Target']\n",
    "X_trainV1 = X_trainV1.drop(['Target'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>pregnants</th>\n",
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       "      <th>0</th>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose_concentration  blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "0                         148.0            72.0                         35.0   \n",
       "1                          85.0            66.0                         29.0   \n",
       "2                         183.0            64.0                         29.0   \n",
       "3                          89.0            66.0                         23.0   \n",
       "4                         137.0            40.0                         35.0   \n",
       "\n",
       "   serum_insulin   BMI  Diabetes_pedigree_function  Age  pregnants  \n",
       "0          125.0  33.6                       0.627   50          6  \n",
       "1          125.0  26.6                       0.351   31          1  \n",
       "2          125.0  23.3                       0.672   32          8  \n",
       "3           94.0  28.1                       0.167   21          1  \n",
       "4          168.0  43.1                       2.288   33          0  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trainV1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>pregnants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>0.639947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>-0.844885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1.233880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>-0.844885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>-1.141852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose_concentration  blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "0                      0.866045       -0.031990                     0.670643   \n",
       "1                     -1.205066       -0.528319                    -0.012301   \n",
       "2                      2.016662       -0.693761                    -0.012301   \n",
       "3                     -1.073567       -0.528319                    -0.695245   \n",
       "4                      0.504422       -2.679076                     0.670643   \n",
       "\n",
       "   serum_insulin       BMI  Diabetes_pedigree_function       Age  pregnants  \n",
       "0      -0.181541  0.166619                    0.468492  1.425995   0.639947  \n",
       "1      -0.181541 -0.852200                   -0.365061 -0.190672  -0.844885  \n",
       "2      -0.181541 -1.332500                    0.604397 -0.105584   1.233880  \n",
       "3      -0.540642 -0.633881                   -0.920763 -1.041549  -0.844885  \n",
       "4       0.316566  1.549303                    5.484909 -0.020496  -1.141852  "
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "mn_X = StandardScaler()\n",
    "numerical_features = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI','Diabetes_pedigree_function','Age','pregnants']\n",
    "temp = mn_X.fit_transform(X_trainV1)\n",
    "X_trainV1 = pd.DataFrame(data=temp, columns=numerical_features, index = X_trainV1.index)\n",
    "X_trainV1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>0.639947</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1.233880</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>-1.141852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose_concentration  blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "0                      0.866045       -0.031990                     0.670643   \n",
       "1                     -1.205066       -0.528319                    -0.012301   \n",
       "2                      2.016662       -0.693761                    -0.012301   \n",
       "3                     -1.073567       -0.528319                    -0.695245   \n",
       "4                      0.504422       -2.679076                     0.670643   \n",
       "\n",
       "   serum_insulin       BMI  Diabetes_pedigree_function       Age  pregnants  \\\n",
       "0      -0.181541  0.166619                    0.468492  1.425995   0.639947   \n",
       "1      -0.181541 -0.852200                   -0.365061 -0.190672  -0.844885   \n",
       "2      -0.181541 -1.332500                    0.604397 -0.105584   1.233880   \n",
       "3      -0.540642 -0.633881                   -0.920763 -1.041549  -0.844885   \n",
       "4       0.316566  1.549303                    5.484909 -0.020496  -1.141852   \n",
       "\n",
       "   Target  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       0  \n",
       "4       1  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并x and y\n",
    "X_trainV1 = pd.concat([X_trainV1, Y_trainV1], axis = 1, ignore_index=False)\n",
    "X_trainV1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Plasma_glucose_concentration', u'blood_pressure',\n",
      "       u'Triceps_skin_fold_thickness', u'serum_insulin', u'BMI',\n",
      "       u'Diabetes_pedigree_function', u'Age', u'pregnants', u'Target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "feat_names = X_trainV1.columns\n",
    "print feat_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#存为csv格式\n",
    "X_trainV1 = pd.DataFrame(columns = feat_names, data = X_trainV1)\n",
    "X_trainV1.to_csv('TrainDataWithOutPre_pima-indians-diabetes.csv',index = False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>0.639947</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1.233880</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>-1.141852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plasma_glucose_concentration  blood_pressure  Triceps_skin_fold_thickness  \\\n",
       "0                      0.866045       -0.031990                     0.670643   \n",
       "1                     -1.205066       -0.528319                    -0.012301   \n",
       "2                      2.016662       -0.693761                    -0.012301   \n",
       "3                     -1.073567       -0.528319                    -0.695245   \n",
       "4                      0.504422       -2.679076                     0.670643   \n",
       "\n",
       "   serum_insulin       BMI  Diabetes_pedigree_function       Age  pregnants  \\\n",
       "0      -0.181541  0.166619                    0.468492  1.425995   0.639947   \n",
       "1      -0.181541 -0.852200                   -0.365061 -0.190672  -0.844885   \n",
       "2      -0.181541 -1.332500                    0.604397 -0.105584   1.233880   \n",
       "3      -0.540642 -0.633881                   -0.920763 -1.041549  -0.844885   \n",
       "4       0.316566  1.549303                    5.484909 -0.020496  -1.141852   \n",
       "\n",
       "   Target  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       0  \n",
       "4       1  "
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainV = pd.read_csv(\"TrainDataWithOutPre_pima-indians-diabetes.csv\")\n",
    "trainV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# trainingData for training \n",
    "#\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "Y_trainV = trainV['Target']\n",
    "X_trainV = trainV.drop(['Target'], axis = 1)\n",
    "lr = LogisticRegression()\n",
    "X_trainV, X_testV, Y_trainV, Y_testV = train_test_split(X_trainV, Y_trainV, test_size=0.2, random_state = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('logloss of each fold is: ', array([ 0.47993907,  0.47857857,  0.48184146,  0.50533766,  0.48077309]))\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "bad operand type for unary -: 'builtin_function_or_method'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-374-1778f0560d3e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#%timeit loss_sparse = cross_val_score(lr, X_train_sparse, y_train, cv=3, scoring='neg_log_loss')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'logloss of each fold is: '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'cv logloss is:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'builtin_function_or_method'"
     ]
    }
   ],
   "source": [
    "loss = cross_val_score(lr, X_trainV, Y_trainV, cv=5, scoring='neg_log_loss')\n",
    "#%timeit loss_sparse = cross_val_score(lr, X_train_sparse, y_train, cv=3, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', -loss.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=4,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters = {'penalty':('l1','l2'), \n",
    "#               'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#              }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(solver='liblinear', class_weight = 'balanced')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss',n_jobs = 4,)\n",
    "grid.fit(X_trainV,Y_trainV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.515103073656\n",
      "{'penalty': 'l2', 'C': 1}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mYI1SKt7FuqqUmJjo7Kbl5Ofnk5KSQnx8PDab0/nLKaWFD8HSx2myewFRwx6g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\nJGO0oy5yqi1+KJdTE0fZ6xwfxyIqoZR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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "# Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "# penaltys = ['l1','l2']\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "#     pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "#     plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
